Exploring chemical space, scaffold diversity, and activity landscape of spleen tyrosine kinase active inhibitors
收藏DataCite Commons2024-05-01 更新2024-08-19 收录
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https://tandf.figshare.com/articles/dataset/Exploring_chemical_space_scaffold_diversity_and_activity_landscape_of_spleen_tyrosine_kinase_active_inhibitors/25730180/1
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This study aims to comprehensively characterize 576 inhibitors targeting Spleen Tyrosine Kinase (SYK), a non-receptor tyrosine kinase primarily found in haematopoietic cells, with significant relevance to B-cell receptor function. The objective is to gain insights into the structural requirements essential for potent activity, with implications for various therapeutic applications. Through chemoinformatic analyses, we focus on exploring the chemical space, scaffold diversity, and structure-activity relationships (SAR). By leveraging ECFP4 and MACCS fingerprints, we elucidate the relationship between chemical compounds and visualize the network using RDKit and NetworkX platforms. Additionally, compound clustering and visualization of the associated chemical space aid in understanding overall diversity. The outcomes include identifying consensus diversity patterns to assess global chemical space diversity. Furthermore, incorporating pairwise activity differences enhances the activity landscape visualization, revealing heterogeneous SAR patterns. The dataset analysed in this work has three activity cliff generators, CHEMBL3415598, CHEMBL4780257, and CHEMBL3265037, compounds with high affinity to SYK are very similar to compounds analogues with reasonable potency differences. Overall, this study provides a critical analysis of SYK inhibitors, uncovering potential scaffolds and chemical moieties crucial for their activity, thereby advancing the understanding of their therapeutic potential.
本研究旨在全面表征靶向脾酪氨酸激酶(Spleen Tyrosine Kinase, SYK)的576种抑制剂。脾酪氨酸激酶是一类主要表达于造血细胞内的非受体酪氨酸激酶,与B细胞受体功能密切相关。本研究的核心目标为阐明强效活性所必需的结构要件,并为该类抑制剂的各类治疗应用提供理论参考。通过化学信息学分析,本研究重点探索该数据集的化学空间、骨架多样性以及构效关系(Structure-Activity Relationships, SAR)。本研究借助ECFP4与MACCS指纹,阐明化合物间的关联,并基于RDKit与NetworkX平台对关联网络进行可视化。此外,化合物聚类与关联化学空间的可视化分析,有助于全面把握整体多样性特征。本研究的结果包括识别共识多样性模式,以评估全局化学空间的多样性水平。进一步引入成对活性差异数据,可优化活性景观可视化效果,揭示异质性构效关系模式。本研究分析的数据集包含3种活性悬崖生成剂:CHEMBL3415598、CHEMBL4780257与CHEMBL3265037,即与SYK高亲和力化合物结构高度相似,但存在显著活性差异的同系物化合物。综上,本研究对SYK抑制剂展开了全面且严谨的分析,发掘了对其活性至关重要的潜在骨架与化学基团,从而推动学界对其治疗潜力的认知。
提供机构:
Taylor & Francis
创建时间:
2024-05-01
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